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. 2022 Apr;43(2):114-119.
doi: 10.1016/j.irbm.2020.07.001. Epub 2020 Jul 3.

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays

N Narayan Das  1 , N Kumar  2 , M Kaur  3 , V Kumar  4 , D Singh  5
Affiliations

Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays

N Narayan Das et al. Ing Rech Biomed. 2022 Apr.

Abstract

The most widely used novel coronavirus (COVID-19) detection technique is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and take 6-9 hours to confirm infection in the patient. Due to less sensitivity of RT-PCR, it provides high false-negative results. To resolve this problem, radiological imaging techniques such as chest X-rays and computed tomography (CT) are used to detect and diagnose COVID-19. In this paper, chest X-rays is preferred over CT scan. The reason behind this is that X-rays machines are available in most of the hospitals. X-rays machines are cheaper than the CT scan machine. Besides this, X-rays has low ionizing radiations than CT scan. COVID-19 reveals some radiological signatures that can be easily detected through chest X-rays. For this, radiologists are required to analyze these signatures. However, it is a time-consuming and error-prone task. Hence, there is a need to automate the analysis of chest X-rays. The automatic analysis of chest X-rays can be done through deep learning-based approaches, which may accelerate the analysis time. These approaches can train the weights of networks on large datasets as well as fine-tuning the weights of pre-trained networks on small datasets. However, these approaches applied to chest X-rays are very limited. Hence, the main objective of this paper is to develop an automated deep transfer learning-based approach for detection of COVID-19 infection in chest X-rays by using the extreme version of the Inception (Xception) model. Extensive comparative analyses show that the proposed model performs significantly better as compared to the existing models.

Keywords: COVID-19; Chest x-ray; Deep learning; Transfer learning.

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Conflict of interest statement

The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper.

Figures

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Graphical abstract
Fig. 1
Fig. 1
Architecture of deep convolution neural network.
Fig. 2
Fig. 2
Architecture of extreme version of Inception (Xception) model (adapted from [34]).
Fig. 3
Fig. 3
Architecture of extreme version of Inception (Xception) model.
Fig. 4
Fig. 4
Training and Validation analyses between the proposed and the inceptionnet V3 models.
See this image and copyright information in PMC

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